{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Downloading the corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from git import Repo\n",
    "import os\n",
    "\n",
    "git_url = \"https://github.com/BlueBrain/corpus-thalamus.git\"\n",
    "repo_dir = os.path.join(os.getcwd(), \"neurocuratorDB\")\n",
    "if not os.path.isdir(repo_dir):\n",
    "    Repo.clone_from(git_url, repo_dir)\n",
    "else:\n",
    "    Repo(repo_dir).remotes.origin.pull()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "\n",
    "# Imports from NeuroCurator code base\n",
    "sys.path.append(os.path.join(os.getcwd(), \"..\"))\n",
    "\n",
    "from glob import glob\n",
    "#from qtNeurolexTree import TreeData\n",
    "from nat.treeData import flatten_list\n",
    "from nat.annotation import Annotation\n",
    "from nat.modelingParameter import getParameterTypeFromID\n",
    "from nat.variable import NumericalVariable\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading all the annotations from the corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "annotations = []\n",
    "for fileName in glob(\"./neurocuratorDB/*.pcr\"):\n",
    "    try:\n",
    "        annotations.extend(Annotation.readIn(open(fileName, \"r\", encoding=\"utf-8\", errors='ignore')))\n",
    "    except:       \n",
    "        print(\"Skipping: \", fileName)    \n",
    "        raise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Verifying basic characteristics of the corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "annotData = {\"Type\":[], \"Nb. of param.\":[], \"Text\":[]}\n",
    "for annot in annotations:\n",
    "    annotData[\"Type\"].append(annot.type)\n",
    "    annotData[\"Nb. of param.\"].append(len(annot.parameters))\n",
    "    annotData[\"Text\"].append(annot.text)\n",
    "annotData = pd.DataFrame(annotData)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of annotations in the corpus: 582\n",
      "First five annotations:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Nb. of param.</th>\n",
       "      <th>Text</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Thalamic\\ncells in higher-order nuclei have higher propensities to discharge in\\nburst as compared with those in first-order nuclei (He &amp; Hu, 2002;\\nRamcharan et al., 2005; Wei et al., 2011).</td>\n",
       "      <td>text</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Note that some of the\\naction potentials in cells exhibiting spikelets (Fig. 12C) were\\ninflected on the rising phase, suggesting an antidromic origin,\\nas would be expected if some of the bursting activity involves\\nan electrically coupled network of pyramidal cell axons (Cun-\\nningham et al. 2004a).</td>\n",
       "      <td>text</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>sustained firing of layer 4 spiny stellate neurons, and\\nlayer 4 VFO, can occur in the model under at least 2 condi-\\ntions: 1) high-conductance AMPA receptors at connections\\nbetween layer 4 spiny stellates (Figs. 7B and 8), or 2) lower\\nconductance AMPA receptors, together with rapid-time-\\ncourse, relatively voltage-independent NMDA receptors, at\\nconnections between spiny stellate cells</td>\n",
       "      <td>text</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>Deficits in EEG s power were also detected in a mouse\\nlacking the Ca V 3.1 isoform [22], the only T channel subtype\\nexpressed in relay neurons [15].</td>\n",
       "      <td>text</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Another important factor in the generation and maintenance of reticular intrinsic oscillations is the aforementioned SK2 or Kcnn2 channel. The vigorous Ca 2+ influx in nRt dendrites originating from Ca V 3.3 channels gates SK2 channels, thereby creating a burst afterhyperpolarization (bAHP) [12,19]. As T channels recover partially from inactivation during bAHPs, nRt cells typically generate a series of low-threshold bursts, with a rhythmicity also seen in nRt of sleeping animals [20,21]. In SK2 À/À mice, this oscillatory discharge is replaced by a single burst followed by a slowly decaying plateau potential [10], demonstrating that the cyclical Ca V 3.3–SK2 channel interaction is necessary for nRt rhythmicity. Conversely, genetic overexpression of SK2 channels resulted in increased bAHPs and prolonged cycles of repetitive bursting [13].\\n</td>\n",
       "      <td>text</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Nb. of param.  \\\n",
       "0              0   \n",
       "1              0   \n",
       "2              0   \n",
       "3              0   \n",
       "4              0   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 Text  \\\n",
       "0                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     Thalamic\\ncells in higher-order nuclei have higher propensities to discharge in\\nburst as compared with those in first-order nuclei (He & Hu, 2002;\\nRamcharan et al., 2005; Wei et al., 2011).   \n",
       "1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Note that some of the\\naction potentials in cells exhibiting spikelets (Fig. 12C) were\\ninflected on the rising phase, suggesting an antidromic origin,\\nas would be expected if some of the bursting activity involves\\nan electrically coupled network of pyramidal cell axons (Cun-\\nningham et al. 2004a).   \n",
       "2                                                                                                                                                                                                                                                                                                                                                                                                                                                                           sustained firing of layer 4 spiny stellate neurons, and\\nlayer 4 VFO, can occur in the model under at least 2 condi-\\ntions: 1) high-conductance AMPA receptors at connections\\nbetween layer 4 spiny stellates (Figs. 7B and 8), or 2) lower\\nconductance AMPA receptors, together with rapid-time-\\ncourse, relatively voltage-independent NMDA receptors, at\\nconnections between spiny stellate cells   \n",
       "3                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              Deficits in EEG s power were also detected in a mouse\\nlacking the Ca V 3.1 isoform [22], the only T channel subtype\\nexpressed in relay neurons [15].   \n",
       "4  Another important factor in the generation and maintenance of reticular intrinsic oscillations is the aforementioned SK2 or Kcnn2 channel. The vigorous Ca 2+ influx in nRt dendrites originating from Ca V 3.3 channels gates SK2 channels, thereby creating a burst afterhyperpolarization (bAHP) [12,19]. As T channels recover partially from inactivation during bAHPs, nRt cells typically generate a series of low-threshold bursts, with a rhythmicity also seen in nRt of sleeping animals [20,21]. In SK2 À/À mice, this oscillatory discharge is replaced by a single burst followed by a slowly decaying plateau potential [10], demonstrating that the cyclical Ca V 3.3–SK2 channel interaction is necessary for nRt rhythmicity. Conversely, genetic overexpression of SK2 channels resulted in increased bAHPs and prolonged cycles of repetitive bursting [13].\\n   \n",
       "\n",
       "   Type  \n",
       "0  text  \n",
       "1  text  \n",
       "2  text  \n",
       "3  text  \n",
       "4  text  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.max_rows = 999\n",
    "pd.options.display.max_colwidth = 1000\n",
    "print(\"Number of annotations in the corpus:\", len(annotData))\n",
    "\n",
    "print(\"First five annotations:\")\n",
    "annotData.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of annotated documents: 113\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of annotated documents:\", len(np.unique([annot.pubId for annot in annotations])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of annotated parameters: 485\n"
     ]
    }
   ],
   "source": [
    "parameters = flatten_list([[(param, annot) for param in annot.parameters] for annot in annotations])\n",
    "parameters, paramAnnot = zip(*parameters)\n",
    "\n",
    "paramDescType = [param.description.type for param in parameters]\n",
    "paramTypes = [getParameterTypeFromID(param.description.depVar.typeId) for param in parameters]\n",
    "paramTypeNames = [param.name for param in paramTypes]\n",
    "paramValues = [param.description.depVar.values.text() if isinstance(param.description.depVar, NumericalVariable)\n",
    "                                               else np.nan for param in parameters]\n",
    "\n",
    "paramTypeIds = [param.ID for param in paramTypes]\n",
    "reqTags      = [[tag.name for tag in param.requiredTags] if len(param.requiredTags) else \"\" for param in parameters]\n",
    "\n",
    "\n",
    "paramContext = [annot.getContext(dbPath=\"../curator_DB\") for annot in paramAnnot]\n",
    "paramText = [annot.text for annot in paramAnnot]\n",
    "pubId = [annot.pubId for annot in paramAnnot]\n",
    "\n",
    "print(\"Number of annotated parameters:\", len(paramTypes))\n",
    "paramValues\n",
    "df = pd.DataFrame({\"Param\":paramTypeNames, \"Values\":paramValues, \n",
    "              \"ID\":paramTypeIds, \"Req. Tag.\":reqTags, \"text\":paramText, \n",
    "               \"context\":paramContext, \"pubID\":pubId}).sort_values(\"ID\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic export to CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "ids = (df[\"ID\"].value_counts()).index[df[\"ID\"].value_counts() > 5]\n",
    "df = df[np.in1d(df[\"ID\"], ids)]\n",
    "df = df[df[\"context\"] != \"\"]\n",
    "df.to_csv(\"params.csv\", index=False, columns=[\"Param\",\"pubID\", \"Values\", \"text\", \"context\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Looking at ionic conductances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First five annotated values:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cell</th>\n",
       "      <th>Species</th>\n",
       "      <th>Transmembrane ionic current</th>\n",
       "      <th>Unit</th>\n",
       "      <th>Values</th>\n",
       "      <th>neuron part</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Thalamus relay cell</td>\n",
       "      <td>[{'name': 'Felis catus domesticus', 'id': 'NIFORG:birnlex_613'}]</td>\n",
       "      <td>Sodium transient fast current</td>\n",
       "      <td>S/cm^2</td>\n",
       "      <td>0.1</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Thalamus relay cell</td>\n",
       "      <td>[{'name': 'Sprague Dawley', 'id': 'NIFORG:birnlex_266'}]</td>\n",
       "      <td>Inward rectifier (h-type) current</td>\n",
       "      <td>nS</td>\n",
       "      <td>9.0</td>\n",
       "      <td>neuron part</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Thalamus relay cell</td>\n",
       "      <td>[{'name': 'Rat', 'id': 'NIFORG:birnlex_160'}]</td>\n",
       "      <td>Sodium transient fast current</td>\n",
       "      <td>mS/cm^2</td>\n",
       "      <td>100.0</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Thalamocortical cell</td>\n",
       "      <td>[{'name': 'Rat', 'id': 'NIFORG:birnlex_160'}]</td>\n",
       "      <td>Potassium inward rectifier (Kir) current</td>\n",
       "      <td>uS/cm^2</td>\n",
       "      <td>20.0</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Thalamus relay cell</td>\n",
       "      <td>[{'name': 'Rat', 'id': 'NIFORG:birnlex_160'}]</td>\n",
       "      <td>Low threshold calcium T-current</td>\n",
       "      <td>pA</td>\n",
       "      <td>109.0</td>\n",
       "      <td>neuron part</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Cell  \\\n",
       "0   Thalamus relay cell   \n",
       "1   Thalamus relay cell   \n",
       "2   Thalamus relay cell   \n",
       "3  Thalamocortical cell   \n",
       "4   Thalamus relay cell   \n",
       "\n",
       "                                                            Species  \\\n",
       "0  [{'name': 'Felis catus domesticus', 'id': 'NIFORG:birnlex_613'}]   \n",
       "1          [{'name': 'Sprague Dawley', 'id': 'NIFORG:birnlex_266'}]   \n",
       "2                     [{'name': 'Rat', 'id': 'NIFORG:birnlex_160'}]   \n",
       "3                     [{'name': 'Rat', 'id': 'NIFORG:birnlex_160'}]   \n",
       "4                     [{'name': 'Rat', 'id': 'NIFORG:birnlex_160'}]   \n",
       "\n",
       "                Transmembrane ionic current     Unit  Values  neuron part  \n",
       "0             Sodium transient fast current   S/cm^2     0.1               \n",
       "1         Inward rectifier (h-type) current       nS     9.0  neuron part  \n",
       "2             Sodium transient fast current  mS/cm^2   100.0               \n",
       "3  Potassium inward rectifier (Kir) current  uS/cm^2    20.0               \n",
       "4           Low threshold calcium T-current       pA   109.0  neuron part  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nat.annotationSearch import ParameterSearch, ConditionAtom\n",
    "#from nat.qtNeurolexTree import TreeData\n",
    "\n",
    "searcher = ParameterSearch(pathDB=\"neurocuratorDB\")\n",
    "searcher.setSearchConditions(ConditionAtom(\"Parameter name\", \"conductance_ion_curr_max\"))\n",
    "searcher.expandRequiredTags = True\n",
    "searcher.onlyCentralTendancy = True\n",
    "\n",
    "resultDF = searcher.search()\n",
    "del resultDF[\"obj_parameter\"]\n",
    "del resultDF[\"obj_annotation\"]\n",
    "del resultDF[\"Context\"]\n",
    "del resultDF[\"Parameter instance ID\"]\n",
    "del resultDF[\"Text\"]\n",
    "del resultDF[\"Parameter type ID\"]\n",
    "del resultDF[\"Result type\"]\n",
    "del resultDF[\"AgeCategories\"]\n",
    "del resultDF[\"Parameter name\"]\n",
    "\n",
    "print(\"First five annotated values:\")\n",
    "resultDF.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Annotated specific conductances:  [-1.         -1.         -4.69897    -3.52287875 -1.30103    -1.\n",
      " -0.46852108 -4.82390874 -2.09691001 -2.25963731 -2.95860731 -2.\n",
      " -1.09691001 -0.74472749 -2.39794001 -5.         -2.52287875 -2.69897\n",
      " -2.29242982 -2.61978876 -1.         -3.30103    -3.82390874 -2.22184875\n",
      " -2.20065945 -4.         -1.         -5.07058107 -1.30103    -3.\n",
      " -3.88605665 -1.30103    -4.65757732 -2.67778071 -3.82390874 -3.60205999\n",
      " -4.15490196 -1.61978876 -2.79588002 -2.39794001 -2.69897    -4.92081875\n",
      " -5.25963731 -3.01772877 -1.95860731 -5.52287875 -2.20065945 -0.39794001\n",
      " -2.69897    -4.52287875 -0.74472749 -3.09691001 -2.         -0.52287875\n",
      " -1.22184875]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/oreilly/python_venv/venv-3.4/lib/python3.5/site-packages/ipykernel_launcher.py:20: UserWarning: Unable to convert between units of \"pA\" and \"S\"\n",
      "/home/oreilly/python_venv/venv-3.4/lib/python3.5/site-packages/ipykernel_launcher.py:20: UserWarning: Unable to convert between units of \"pA/um**2\" and \"S\"\n",
      "/home/oreilly/python_venv/venv-3.4/lib/python3.5/site-packages/ipykernel_launcher.py:20: UserWarning: Unable to convert between units of \"nA\" and \"S\"\n"
     ]
    }
   ],
   "source": [
    "import quantities as pq #q.rescale('US_survey_acre')\n",
    "import warnings\n",
    "\n",
    "specificConductances = []\n",
    "conductances = []\n",
    "isSpecific = []\n",
    "allConductances = []\n",
    "\n",
    "for v, u in zip(resultDF[\"Values\"], resultDF[\"Unit\"]):\n",
    "    try:\n",
    "        value = pq.Quantity(v, u).rescale('S/cm**2')\n",
    "        specificConductances.append(value)\n",
    "        isSpecific.append(True)\n",
    "    except ValueError:\n",
    "        try:\n",
    "            value = pq.Quantity(v, u).rescale('S')\n",
    "            conductances.append(value)\n",
    "            isSpecific.append(False)\n",
    "        except ValueError as e:\n",
    "            warnings.warn(str(e))\n",
    "            value = np.nan\n",
    "            isSpecific.append(False)\n",
    "\n",
    "    allConductances.append(float(value))\n",
    "        \n",
    "resultDF[\"ValuesNorm\"] = np.log10(allConductances)\n",
    "resultDF[\"isSpecific\"] = isSpecific \n",
    "print(\"Annotated specific conductances: \", resultDF[\"ValuesNorm\"][resultDF[\"isSpecific\"]].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Specific conductances (S/cm**2)')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f203a9a7320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "g = sns.violinplot(x=\"Cell\", y=\"ValuesNorm\", data=resultDF[resultDF[\"isSpecific\"]], bw=0.5)\n",
    "g.set_xticklabels(g.get_xticklabels(), rotation=90)\n",
    "g.set_xlabel(\"\")\n",
    "\n",
    "values  = g.get_yticks()\n",
    "labels = [('%.0e' % 10**nb) for nb in values]\n",
    "g.set_yticklabels(labels)\n",
    "g.set_ylabel(\"Specific conductances (S/cm**2)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f20372a8b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pylab as plt\n",
    "import seaborn as sns\n",
    "g = sns.violinplot(y=\"Transmembrane ionic current\", x=\"ValuesNorm\", \n",
    "                   data=resultDF[resultDF[\"isSpecific\"]], bw=0.25)\n",
    "g = sns.swarmplot(y=\"Transmembrane ionic current\", x=\"ValuesNorm\", \n",
    "                   data=resultDF[resultDF[\"isSpecific\"]])\n",
    "g.set_yticklabels(g.get_yticklabels())#, rotation=90)\n",
    "g.set_ylabel(\"\")\n",
    "g.set_xlabel(\"Specific conductance ($S/cm^2$)\")\n",
    "\n",
    "values  = g.get_xticks()\n",
    "labels = [('%.0e' % 10**nb) for nb in values]\n",
    "g.set_xticklabels(labels) #x, labels, rotation='vertical')\n",
    "plt.savefig('example_currents.png', bbox_inches='tight', transparent=True, dpi=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Looking at resting membrane potential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AgeCategories</th>\n",
       "      <th>Cell</th>\n",
       "      <th>Parameter name</th>\n",
       "      <th>Species</th>\n",
       "      <th>Unit</th>\n",
       "      <th>Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[]</td>\n",
       "      <td>Cell</td>\n",
       "      <td>resting_membrane_potential</td>\n",
       "      <td>[]</td>\n",
       "      <td>mV</td>\n",
       "      <td>-70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[]</td>\n",
       "      <td>Thalamocortical cell</td>\n",
       "      <td>resting_membrane_potential</td>\n",
       "      <td>[{'name': 'Long Evans Rat', 'id': 'NIFORG:nlx_organ_20081201'}, {'name': 'Long Evans Rat', 'id': 'NIFORG:nlx_organ_20081201'}]</td>\n",
       "      <td>mV</td>\n",
       "      <td>-60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[]</td>\n",
       "      <td>Thalamocortical cell</td>\n",
       "      <td>resting_membrane_potential</td>\n",
       "      <td>[{'name': 'Wistar Rat', 'id': 'NIFORG:birnlex_254'}]</td>\n",
       "      <td>mV</td>\n",
       "      <td>-58.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[]</td>\n",
       "      <td>Thalamocortical cell</td>\n",
       "      <td>resting_membrane_potential</td>\n",
       "      <td>[{'name': 'Rat', 'id': 'NIFORG:birnlex_160'}, {'name': 'Rat', 'id': 'NIFORG:birnlex_160'}]</td>\n",
       "      <td>mV</td>\n",
       "      <td>-65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[]</td>\n",
       "      <td>Cell</td>\n",
       "      <td>resting_membrane_potential</td>\n",
       "      <td>[{'name': 'Mouse', 'id': 'NIFORG:birnlex_167'}]</td>\n",
       "      <td>mV</td>\n",
       "      <td>-59.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  AgeCategories                  Cell              Parameter name  \\\n",
       "0            []                  Cell  resting_membrane_potential   \n",
       "1            []  Thalamocortical cell  resting_membrane_potential   \n",
       "2            []  Thalamocortical cell  resting_membrane_potential   \n",
       "3            []  Thalamocortical cell  resting_membrane_potential   \n",
       "4            []                  Cell  resting_membrane_potential   \n",
       "\n",
       "                                                                                                                          Species  \\\n",
       "0                                                                                                                              []   \n",
       "1  [{'name': 'Long Evans Rat', 'id': 'NIFORG:nlx_organ_20081201'}, {'name': 'Long Evans Rat', 'id': 'NIFORG:nlx_organ_20081201'}]   \n",
       "2                                                                            [{'name': 'Wistar Rat', 'id': 'NIFORG:birnlex_254'}]   \n",
       "3                                      [{'name': 'Rat', 'id': 'NIFORG:birnlex_160'}, {'name': 'Rat', 'id': 'NIFORG:birnlex_160'}]   \n",
       "4                                                                                 [{'name': 'Mouse', 'id': 'NIFORG:birnlex_167'}]   \n",
       "\n",
       "  Unit Values  \n",
       "0   mV    -70  \n",
       "1   mV    -60  \n",
       "2   mV -58.41  \n",
       "3   mV    -65  \n",
       "4   mV -59.11  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nat.annotationSearch import ParameterSearch, ConditionAtom\n",
    "\n",
    "searcher = ParameterSearch(pathDB=\"neurocuratorDB\")\n",
    "searcher.setSearchConditions(ConditionAtom(\"Parameter name\", \"resting_membrane_potential\"))\n",
    "searcher.expandRequiredTags = True\n",
    "searcher.onlyCentralTendancy = True\n",
    "\n",
    "resultDF2 = searcher.search()\n",
    "del resultDF2[\"obj_parameter\"]\n",
    "del resultDF2[\"obj_annotation\"]\n",
    "del resultDF2[\"Context\"]\n",
    "del resultDF2[\"Parameter instance ID\"]\n",
    "del resultDF2[\"Text\"]\n",
    "del resultDF2[\"Parameter type ID\"]\n",
    "del resultDF2[\"Result type\"]\n",
    "\n",
    "resultDF2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f20368f01d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "resultDF2[\"Values\"] = [np.mean(val) for val in resultDF2[\"Values\"]]\n",
    "g = sns.violinplot(x=\"Cell\", y=\"Values\", data=resultDF2, bw=0.1)\n",
    "g.set_xticklabels(g.get_xticklabels(), rotation=90)\n",
    "g.set_xlabel(\"\")\n",
    "#g.set_yscale(\"log\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
